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Image Steganography Using Fractal Cover and Combined Chaos-DNA Based Encryption

Author

Listed:
  • Asha Durafe

    (Shah & Anchor Kutchhi Engineering College)

  • Vinod Patidar

    (Sir Padampat Singhania University)

Abstract

To address the need for secure digital image transmission an algorithm that fulfils all prominent prerequisites of a steganography technique is developed. By incorporating the salient features of fractal cover images, dual-layer encryption using the standard chaotic map and DNA-hyperchaotic cryptography along with DWT-SVD embedding, key aspects like robustness, better perceptual quality and high payload capacity are targeted to build a blind colour image steganography algorithm in this work. A fractal cover image is used to hide a DNA-chaotic encrypted colour image using DWT-SVD embedding method. A two-dimensional standard chaotic map, which exhibits robust chaos for a very large range of parameter, is used to generate the pseudo-random number sequences of cryptographic qualities. One of the core novelty of the proposed method is the 2 layers chaotic encryption method to generate the DNA encrypted secret image which is finally embedded in a fractal cover image using DWT-SVD transform domain technique capable of withstanding the false positive attack. The comprehensive statistical security tests and the standard evaluation benchmarks depict that this efficient yet simple hybrid steganography algorithm is highly robust as well as sustainable against removal, geometrical, image enhancement and histogram attacks, offers better perceptual image quality and also contributes high perceptual quality of the extracted image.

Suggested Citation

  • Asha Durafe & Vinod Patidar, 2024. "Image Steganography Using Fractal Cover and Combined Chaos-DNA Based Encryption," Annals of Data Science, Springer, vol. 11(3), pages 855-885, June.
  • Handle: RePEc:spr:aodasc:v:11:y:2024:i:3:d:10.1007_s40745-022-00457-x
    DOI: 10.1007/s40745-022-00457-x
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    References listed on IDEAS

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    1. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
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